Proceedings of the 2018 Conference of the North American Chapter Of the Association for Computational Linguistics: St 2018
DOI: 10.18653/v1/n18-4011
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Towards Generating Personalized Hospitalization Summaries

Abstract: Most of the health documents, including patient education materials and discharge notes, are usually flooded with medical jargons and contain a lot of generic information about the health issue. In addition, patients are only provided with the doctor's perspective of what happened to them in the hospital while the care procedure performed by nurses during their entire hospital stay is nowhere included. The main focus of this research is to generate personalized hospital-stay summaries for patients by combining… Show more

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Cited by 4 publications
(2 citation statements)
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“…4 For example, a title keyword search for 'personali' or 'personaliz' returns 124 articles from the ACL Anthology and a further 10 from the arXiv Computation and Language (cs.CL) subclass. These systems cover a wide range of tasks including dialogue [127,157,36,39,41,109,133,146,149,206,238,244], recipe or diet generation [147,87,159], summarisation [215,240], machine translation [156,153,194,237], QA [137,193], search and information retrieval [4,40,59,70,245], sentiment analysis [80,155,226], domain classification [129,114,113], entity resolution [132], and aggression or abuse detection [107,108]; and are applied to a number of societal domains such as education [118,163,241], medicine [3,15,225,235] and news consumption…”
Section: From Implicit To Explicit Personalisationmentioning
confidence: 99%
See 1 more Smart Citation
“…4 For example, a title keyword search for 'personali' or 'personaliz' returns 124 articles from the ACL Anthology and a further 10 from the arXiv Computation and Language (cs.CL) subclass. These systems cover a wide range of tasks including dialogue [127,157,36,39,41,109,133,146,149,206,238,244], recipe or diet generation [147,87,159], summarisation [215,240], machine translation [156,153,194,237], QA [137,193], search and information retrieval [4,40,59,70,245], sentiment analysis [80,155,226], domain classification [129,114,113], entity resolution [132], and aggression or abuse detection [107,108]; and are applied to a number of societal domains such as education [118,163,241], medicine [3,15,225,235] and news consumption…”
Section: From Implicit To Explicit Personalisationmentioning
confidence: 99%
“…Additionally, a personalised LLM could store background context and form epistemic priors about a user. This knowledge adaption may be particularly relevant in specific domains, for example in (i) education, where a personalised LLM tutor is aware of a user's current knowledge and learning goals [118], or could adapt learning pathways to specific neuro-developmental disorders [22]; (ii) healthcare, where a personalised model has context on a user's medical history for personalised summaries [3] or advice; (iii) financial, where a personalised model knows a user's risk tolerance and budgetary constraints; or (iv) legal, where a model conditions its responses based on a end-user's jurisdiction. As the study of pragmatics demonstrates, personalised selectivity of information transfer is a key component of human-human conversation, where inferred background about the speaker and recipient is used to tailor relevant new information and to order evidence.…”
Section: Ib2 Utilitymentioning
confidence: 99%